[1]}, [2]} force the sparsity of the attention with sparsemax [3]}, so as to promote the faithfulness of their attention as rationales.
The interlocking problem still persists in this framework, because the loss landscape remains concave (refer to our arguments in Section REF &REF ).
Specifically, since the predictor would not see the sentences that receive 0 attention weights, it tends to underfit these sentences. As a result, the generator does not have the incentive to assign positive weights to the sentences that are previously assigned zero weights, thus is prone to selecting the same sentences.
